Julia package for xtensor-julia



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Julia package for the xtensor-julia library, the Julia bindings for xtensor.

  • xtensor is a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing.
  • xtensor-julia enables inplace use of julia arrays in C++ with all the benefits from xtensor

The Julia bindings for xtensor are based on the CxxWrap.jl C++ library.


using Pkg; Pkg.add("Xtensor");


To get started with using Xtensor.jl and xtensor-julia, check out the full documentation



xtensor-julia offers two container types wrapping julia arrays inplace to provide an xtensor semantics

  • jltensor
  • jlarray.

Both containers enable the numpy-style APIs of xtensor (see the numpy to xtensor cheat sheet).

  • On the one hand, jlarray has a dynamic number of dimensions. It can be reshaped dynamically and the new shape is reflected on the Julia side.

  • On the other hand jltensor has a compile time number of dimensions, specified with a template parameter. Shapes of jltensor instances are stack allocated, making jltensor a significantly faster expression than jlarray.

Example 1: Use an algorithm of the C++ standard library with Julia array.

C++ code

#include                         // Standard library import for std::accumulate
#include                    // libcxxwrap import to define Julia bindings
#include "xtensor-julia/jltensor.hpp"     // Import the jltensor container definition
#include "xtensor/xmath.hpp"              // xtensor import for the C++ universal functions

double sum_of_sines(xt::jltensor m)
    auto sines = xt::sin(m);  // sines does not actually hold values.
    return std::accumulate(sines.cbegin(), sines.cend(), 0.0);

JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
    mod.method("sum_of_sines", sum_of_sines);

Julia Code

using xtensor_julia_test

arr = [[1.0 2.0]
       [3.0 4.0]]

s = sum_of_sines(arr)



Example 2: Create a numpy-style universal function from a C++ scalar function

C++ code

#include "xtensor-julia/jlvectorize.hpp"

double scalar_func(double i, double j)
    return std::sin(i) - std::cos(j);

JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
    mod.method("vectorized_func", xt::jlvectorize(scalar_func));

Julia Code

using xtensor_julia_test

x = [[ 0.0  1.0  2.0  3.0  4.0]
     [ 5.0  6.0  7.0  8.0  9.0]
     [10.0 11.0 12.0 13.0 14.0]]
y = [1.0, 2.0, 3.0, 4.0, 5.0]
z = vectorized_func(x, y)


[[-0.540302  1.257618  1.89929   0.794764 -1.040465],
 [-1.499227  0.136731  1.646979  1.643002  0.128456],
 [-1.084323 -0.583843  0.45342   1.073811  0.706945]]

Building the HTML Documentation

xtensor-julia's documentation is built with three tools

While doxygen must be installed separately, you can install breathe by typing

pip install breathe

Breathe can also be installed with mamba (or conda)

mamba install -c conda-forge breathe

Finally, build the documentation with

make html

from the docs subdirectory.

Running the C++ tests

From deps/build

cmake -D JlCxx_DIR=/path/to/.julia/v1.1/CxxWrap/deps/usr/lib/cmake/JlCxx -D BUILD_TESTS=ON ..

Dependencies on xtensor, xtensor-julia, and CxxWrap

Xtensor.jl depends on xtensor-julia, xtensor and CxxWrap libraries

Xtensor.jl xtensor xtensor-julia CxxWrap
master >=0.20.8,<0.21 0.8.4 >=0.8.1,<0.9
0.8.2 >=0.20.8,<0.21 0.8.4 >=0.8.1,<0.9
0.8.1 >=0.20.4,<0.21 0.8.2 >=0.8.1,<0.9
0.8.0 >=0.20.4,<0.21 0.8.2 >=0.8.1,<0.9
0.7.0 >=0.19.0,<0.20 0.7.0 >=0.8.1,<0.9
0.6.2 >=0.18.3,<0.19 0.6.2 >=0.8.1,<0.9
0.6.1 >=0.18.1,<0.19 0.6.0 >=0.8.1,<0.9
0.6.0 >=0.18.1,<0.19 0.6.0 >=0.8.1,<0.9

These dependencies are automatically resolved when using the Julia package manager.


We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.

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